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Access to Unlabeled Data can Speed up Prediction Time
2011
International Conference on Machine Learning
Semi-supervised learning (SSL) addresses the problem of training a classifier using a small number of labeled examples and many unlabeled examples. Most previous work on SSL focused on how availability of unlabeled data can improve the accuracy of the learned classifiers. In this work we study how unlabeled data can be beneficial for constructing faster classifiers. We propose an SSL algorithmic framework which can utilize unlabeled examples for learning classifiers from a predefined set of
dblp:conf/icml/UrnerSB11
fatcat:oazgr5dk2rhpniy4xgdjwmauqy